Bayesian Quantile Joint Modeling of Multivariate Longitudinal and Time-to-Event Data

Explore a Bayesian quantile joint modeling approach for analyzing multivariate longitudinal and time-to-event data in this conference talk from the International Conference on Bayesian Statistics 2024. Delve into the application of linear quantile mixed models for non-Gaussian outcomes and time-varying covariates. Learn how this method was used to analyze data from an Acute Lymphocytic Leukemia (ALL) maintenance study, examining the effects of standard drugs on biomarkers and relapse time. Discover the advantages of using Asymmetric Laplace Distribution (ALD) and a Gibbs sampler algorithm for estimating regression coefficients. Gain insights into the relationships between lymphocyte count, neutrophil count, platelet count, and relapse chances, as well as the effects of 6MP and MTx drugs across different quantiles. Understand the effectiveness of this approach through presented simulation studies and its potential applications in medical research and beyond.

Kiranmoy Das: A Bayesian quantile joint modeling of multivariate longitudinal and... #ICBS2024